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This thesis tackles the challenge of real-time image analysis in resource-constrained embedded systems, focusing specifically on Micro Aerial Vehicle (MAV) applications. The primary objective of this research is to design a software architecture that integrates features like modularity, real-time capabilities, robustness, and adaptability to meet the demands. The study proposes a unique software architecture based on blackboard and microservices architectures, that facilitates the key strengths from both paradigms, while mitigating their individual limitations. Additionally, it leverages shared memory inter-process communication mechanism for implementing centralized knowledge base of the blackboard, and realizing the API of the microservices architecture. The computer vision system tasks are decomposed into smaller pieces, and developed and implemented as loosely coupled individual software components. The thesis contribution lies in an efficient architecture for real-time image analysis on safety-critical and resource-constrained MAV platforms. The architecture provides an efficient and real-time-capable backbone and offers modularity and reusability for diverse applications.